I’m excited to announce that registration for the first workshop on Data and Algorithmic Transparency is now open. The workshop will take place at NYU on Nov 19. It convenes an emerging interdisciplinary community that seeks transparency and oversight of data-driven algorithmic systems through empirical research. Despite the short notice of the workshop’s announcement (about […]
The workshop on Data and Algorithmic Transparency
August 10, 2016 by
From online advertising to Uber to predictive policing, algorithmic systems powered by personal data affect more and more of our lives. As our society begins to grapple with the consequences of this shift, empirical investigation of these systems has proved vital to understand the potential for discrimination, privacy breaches, and vulnerability to manipulation. This emerging […]
Revealing Algorithmic Rankers
August 5, 2016 by
By Julia Stoyanovich (Assistant Professor of Computer Science, Drexel University) and Ellen P. Goodman (Professor, Rutgers Law School) ProPublica’s story on “machine bias” in an algorithm used for sentencing defendants amplified calls to make algorithms more transparent and accountable. It has never been more clear that algorithms are political (Gillespie) and embody contested choices (Crawford), […]
A Peek at A/B Testing in the Wild
May 26, 2016 by
[Dillon Reisman was previously an undergraduate at Princeton when he worked on a neat study of the surveillance implications of cookies. Now he’s working with the WebTAP project again in a research + engineering role. — Arvind Narayanan] In 2014, Facebook revealed that they had manipulated users’ news feeds for the sake of a psychology study […]
